# Research Fellowship - Mechanistic Interpretability at Vmax AI Corp

Research Fellowship - Mechanistic Interpretability at Vmax AI Corp in SF. Full-time. Posted May 20, 2026.

- Company: Vmax AI Corp
- Location: SF
- Job type: Full-time
- Posted: 2026-05-20
- Apply: https://jobs.ashbyhq.com/vmax/7344d5fd-6fa3-4bce-b7bd-e73aa4dd568b/application
- Source: https://jobmagic.ai/jobs/research-fellowship-mechanistic-interpretability-vmax-ai-corp-y21fd3

## Requirements

- Currently enrolled in a PhD program in machine learning, computer science, artificial intelligence, computational neuroscience, mathematics, or a related technical field. Exceptional candidates with equivalent research experience may also be considered.
- Track record of research excellence or strong research promise, demonstrated through publications, preprints, open-source work, technical projects, competitions, or publicly available artifacts.
- Working understanding of reinforcement learning.
- Familiarity with mechanistic interpretability, representation analysis, or empirical methods for understanding neural networks.
- Strong programming ability in Python and experience with at least one major ML framework such as PyTorch or JAX.
- Clear written and verbal communication of technical ideas.

## Description

### **About *Vmax***

*Vmax* is an applied research lab developing AI capable of open-ended learning. We are building systems to exceed humans in all capacities by optimizing beyond the local maxima of learning from human expertise.

### About the role

LLMs are fantastically powerful and there is a rapidly growing corpus of work devoted to understanding their internal representations and computations. We use the tools of mechanistic interpretability to enhance reinforcement learning by generating intrinsic rewards as a supplement or alternative to downstream human-generated verifiers. 
This 3 to 6 month fellowship is for PhD students or equivalent early-career researchers who want to work at the intersection of mechanistic interpretability and reinforcement learning. You will own a focused research project, work closely with Vmax technical staff, and contribute to research publications.

### Responsibilities

- Develop mechanistic interpretability methods for understanding internal representations, features, circuits, and computations in language models and agents.

- Investigate how model internals can be used to generate intrinsic rewards, auxiliary objectives, diagnostics, or training signals for reinforcement learning.

- Design and run experiments that test whether interpretability-derived signals improve learning, exploration, generalization, robustness, or sample efficiency.

- Compare internally derived rewards against baselines such as human-generated verifiers, reward models, task-level outcome rewards, and standard intrinsic motivation methods.

- Use techniques such as probing, activation analysis, sparse autoencoders, causal interventions, feature attribution, or representation analysis to study model behavior.

- Analyze failure modes, including reward hacking, spurious features, non-causal correlations, objective misspecification, and overfitting to narrow evaluation distributions.

- Build research code, evaluation harnesses, and experimental infrastructure that make results reproducible and useful to the broader team.

- Communicate research progress clearly through written updates, internal presentations, and final project outputs.

### Role Requirements

- Currently enrolled in a PhD program in machine learning, computer science, artificial intelligence, computational neuroscience, mathematics, or a related technical field. Exceptional candidates with equivalent research experience may also be considered.

- Track record of research excellence or strong research promise, demonstrated through publications, preprints, open-source work, technical projects, competitions, or publicly available artifacts.

- Working understanding of reinforcement learning.

- Familiarity with mechanistic interpretability, representation analysis, or empirical methods for understanding neural networks.

- Strong programming ability in Python and experience with at least one major ML framework such as PyTorch or JAX.

- Clear written and verbal communication of technical ideas.

### Nice to have

- Experience with LLM post-training methods 

- Familiarity with intrinsic motivation, unsupervised RL, auxiliary objectives, representation learning for RL, or curiosity-driven learning.

- Experience with scalable ML experimentation, distributed training, experiment tracking, or reproducible research infrastructure.

- Interest in turning mechanistic understanding into practical training methods, rather than only analyzing models after training.

### **Role specific location policy**

- This role is based in our San Francisco office; for exceptional candidates we are willing to consider a hybrid arrangement